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共 79661 条记录，以下是181-190 订阅

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 ieee.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Bridging the Machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 ieee.

关键词： Constrained optimization

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Xupei Qin, Hanlin Li, Jingjing Geng, Jinni Gao, Zihan Yu, Yue School of Optoelectronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Infrared Technology and System Xi'an710071 China

ISBN: (纸本)9798350368741

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available. © 2025 ieee.

关键词： Diffusion Model image colorization Spatial-Frequency Interaction Synthetic aperture radar

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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Baisong Wang, Xingwang Xu, Haixiao College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Krowledge Engineering Ministry of Education Jilin University China

ISBN: (纸本)9798350368741

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba. © 2025 ieee.

关键词： hyperspectral image Mamba spectral super-resolution state space model

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Efficient Global Attention and Correlation-Aware Fusion for Hyperspectral image Classification

Efficient Global Attention and Correlation-Aware Fusion for ...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Hongkang Huang, Shao-Lun Kuruoglu, Ercan Engin Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350368741

Hyperspectral imaging offers extensive spectral and spatial information. However, effectively utilizing this data for accurate classification remains a challenge. This study introduced the CASSX-Net, a novel framework designed to capture both short- and long-range dependencies in HSI data for land cover classification. The network combined a dual spectral-spatial feature extraction mechanism with a multi-head cross-attention module to leverage local and global feature interactions. By combining convolutional layers for short-range feature extraction with cross-attention mechanisms for long-range dependencies, the CASSX-Net addressed the intricate spectral-spatial correlations often missed by traditional CNNs. In addition, the maximal correlation fusion strategy optimally integrated the features from various pathways, improving the ability of the model to distinguish between classes with similar spectral signatures. The rigorous evaluation of four benchmark HSI datasets, including Pavia University, Pavia Centre, Salinas, and Houston 2018, demonstrated that the proposed framework consistently achieved the state-of-the-art performance, surpassing the existing methods in terms of classification accuracy and advancing the HSI land cover classification. © 2025 ieee.

关键词： convolutional neural network cross attention mechanism deep learning dual path model Hyperspectral image classification

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DRANet: Dual-threshold Guided Reliability Aware Network for Semi-Supervised image Semantic Segmentation

DRANet: Dual-threshold Guided Reliability Aware Network for ...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Qiu, Lizhe Zhang, Mingyang Zheng, Yuan College of Computer Science Inner Mongolia University Huhhot China

ISBN: (纸本)9798350368741

Different from supervised semantic segmentation task, semi-supervised semantic segmentation (SSSS) aims to alleviate the burden of time-consuming pixel-wise manual labeling. Although existing methods have achieved the promising performance with a small amount of labeled images, they still suffer from an insufficient mining on a large amount of unlabeled images due to simple evaluation way for them. To address this issue, we propose a novel Dual-threshold Guided Reliability Aware Network (DRANet) for SSSS task. To comprehensively evaluate the reliability of unlabeled images, we provide a dual-threshold guided augmentation module (DGAM), which not only introduces two predefined thresholds to group unlabeled images with different confidences according to their learning status, but also performs different data augmentations on unlabeled images based on their evaluation results. In particular, for an unlabeled image with higher confidence, a double-stream augmentation strategy is adopted, leading to a more sufficient learning for it. Experiments on Cityscapes and Pascal VOC 2012 datasets confirm the superiority of our DRANet over existing methods. Code is available at https://***/ZY-IMUCV/DRANet_QLZ_2024. © 2025 ieee.

关键词： data augmentation Semantic segmentation semi-supervised learning teacher-student model

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Exploiting Foundation Models for Label-Efficient Few-Shot Learning via Feature Coupling: A Case Study of cardiac CT Segmentation

Exploiting Foundation Models for Label-Efficient Few-Shot Le...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chen, Wei Li, Chen Zhou, Wenjuan Li, Yuhang Guo, Tianhang Tang, Yuhua College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

The scarcity of labeled data poses a significant challenge for deep learning-based medical image segmentation. To address this, this study introduces the novel Foundation Model-based Few-Shot Segmentation (FM-FSS) paradigm. FM-FSS capitalizes on the knowledge distilled from pre-trained foundation models, such as the Segment Anything Model, to enhance segmentation performance in few-shot scenarios. The paradigm designs a feature coupling module that synergizes SAM's powerful feature extraction capabilities with nnU-Net's self-configuration strategy, enabling accurate segmentation with minimal labeled data and optional manual prompt inputs. Extensive experiments on a publicly available cardiac CT dataset demonstrate that FM-FSS outperforms state-of-the-art segmentation models. With only 20 labeled images, our method achieves an average Dice score of 94.33% and an ASD of 1.10 mm. Moreover, FM-FSS maintains its label-efficient performance in a one-shot setup, reducing the annotation requirements by at least fourfold. The code and pre-trained models will be released upon acceptance. © 2025 ieee.

关键词： Few-shot learning Foundation model image segmentation SAM

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BIAWDiff: Enhancing Low-Light images with Bio-Inspired Attention and Wavelet Diffusion

BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Atten...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Zeyu Yang, Sheng Yang, Hanxiang Tang, Xiongxin Wu, Fengge Xu, Fanjiang Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggle with balancing brightness enhancement and detail preservation, leading to issues such as overexposure, artifacts, and loss of high-frequency details. To address these challenges, we propose a novel method, Bio-Inspired Attention and Wavelet Diffusion (BIAWDiff), that integrates Retinex theory with bio-inspired attention and wavelet-based diffusion models to enhance low-light images. BIAWDiff consists of three key modules: the Initial Light Restoration (ILR) module for brightness enhancement and noise reduction, the Rod Cell-Inspired Attention Refinement (RCAR) module for luminance refinement, and the Detail Refinement (DR) module for restoring high-frequency details. Experimental results demonstrate that BIAWDiff outperforms existing techniques, achieving superior results in brightness enhancement, noise reduction, and detail preservation, with an average PSNR increase of 5.1% and SSIM improvement of 3.2% on paired datasets, and a reduction in NIQE and BRISQUE by 7.4% and 8.6% on unpaired datasets. © 2025 ieee.

关键词： diffusion models Low-light image enhancement Retinex theory transformer wavelet domain

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A Novel Split Deep Unfolding Transformer for Pan-Sharpening

A Novel Split Deep Unfolding Transformer for Pan-Sharpening

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chen, Jiannan Jiang, Zhizhuo Wang, Xueqian Li, Yaowen Wang, Huajie Liu, Yu Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Pan-sharpening is a commonly employed strategy to obtain high-resolution multispectral (HRMS) images. Existing deep unfolding networks for pan-sharpening suffer from ineffectively establishing the relationship between panchromatic (PAN) images and generated noisy HRMS (GN-HRMS) images in PAN-guided image denoising, lacking the support of physical models. In this paper, we first design a degradation-fusion-aware unfolding framework (DF-UF) to separate the processing of PAN-prior in PAN-guided image denoising into an individual module, PAN-prior processor, for better integrating physical models. Then, we derive a flexible intensity-hue-saturation (F-IHS) to act as the PAN-prior processor, which models the relationship between PAN images and GN-HRMS images in terms of intensity components through the intensity-hue-saturation (IHS) theory. Finally, plugging F-IHS into DF-UF, we propose a degradation-intensity-aware unfolding transformer (DIUT) to address the problem of incomplete utilization of PAN images in the denoising process. Extensive experiments on diverse scenes show that the performance of DIUT surpasses existing state-of-the-art methods. © 2025 ieee.

关键词： deep unfolding network intensity-hue-saturation pan-sharpening transformer

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Joint Semantic Segmentation of Optical and SAR image in Hazy Environments via Cross-modal Information Rectification and Cross-attention Fusion

Joint Semantic Segmentation of Optical and SAR Image in Hazy...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Fan, Xinyue Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed for clear images and struggle in harsh weather. 2) Current fusion methods insufficiently capture multi-modal information correlation. This paper presents a joint semantic segmentation of optical and SAR in hazy environments network that incorporates channel fusion for feature enhancement and cross-attention for feature fusion, enabling efficient segmentation of hazy optical images. First, we design a channel-guided crossmodal information correction module. This module regards fog as noise and uses high-confidence features of one modality to calibrate the other modality, thereby reducing the impact of fog. Secondly, to address the issue of insufficient fusion of multimodal information, we introduce a cross-attention fusion module to effectively combine the complementary information from two streams leveraging the large receptive field enabled by self-attention. The implementation results show that this method has better results for the semantic segmentation of blurry remote sensing images. © 2025 ieee.

关键词： multi-modal fusion optical images remote sensing SAR images Semantic segmentation

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